1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CR2020★ 1 cited
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Cheng Hong, Zhicong Huang, Wen-jie Lu +4
Machine learning (ML) methods have been widely used in genomic studies. However, genomic data are often held by different stakeholders (e.g. hospitals, universities, and healthcare…
cs.LG2019★ 1 cited
Detecting Spiky Corruption in Markov Decision Processes
Jason Mancuso, Tomasz Kisielewski, David Lindner +1
Current reinforcement learning methods fail if the reward function is imperfect, i.e. if the agent observes reward different from what it actually receives. We study this problem w…
cs.LG2018
A generic framework for privacy preserving deep learning
Theo Ryffel, Andrew Trask, Morten Dahl +4
We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valu…